Introduction to turbulence/Statistical analysis/Multivariate random variables
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where as before capital letters are usd to represent the mean values. Clearly the fluctuating quantities <math>u'</math> and <math>v'</math> are random variables with zero man. | where as before capital letters are usd to represent the mean values. Clearly the fluctuating quantities <math>u'</math> and <math>v'</math> are random variables with zero man. | ||
| - | A | + | A positive value of <math>\left\langle u'v' \right\rangle </math> indicates that <math>u'</math> and <math>v'</math> tend to vary together. A negative value indicates value indicates that when one variable is increasing the other tends to be decreasing. A zero value of <math>\left\langle u'v' \right\rangle </math> indicates that there is no correlation between <math>u'</math> and <math>v'</math>. As will be seen below, it does ''not'' mean that they are statistically independent. |
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| + | It is sometimes more convinient to deal with values of the cross-variances which have ben normalized by the appropriate variances. Thus the ''correlation coefficient'' is defined as: | ||
=== The bi-variate normal (or Gaussian) distribution === | === The bi-variate normal (or Gaussian) distribution === | ||
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Revision as of 08:23, 2 June 2006
Joint pdfs and joint moments
Often it is importamt to consider more than one random variable at a time. For example, in turbulence the three components of the velocity vector are interralated and must be considered together. In addition to the marginal (or single variable) statistical moments already considered, it is necessary to consider the joint statistical moments.
For example if 
and 
are two random variables, there are three second-order moments which can be defined 
, 
, and 
. The product moment is called the cross-correlation or cross-covariance. The moments and are referred to as the covariances, or just simply the variances. Sometimes is also referred to as the correlation.
In a manner similar to that used to build-up the probabilility density function from its measurable counterpart, the histogram, a joint probability density function (or jpdf),
, can be built-up from the joint histogram. Figure 2.5 illustrates several examples of jpdf's which have different cross correlations. For convenience the fluctuating variables 
and 
can be defined as
|
| (2) |
|
| (2) |
where as before capital letters are usd to represent the mean values. Clearly the fluctuating quantities and are random variables with zero man.
A positive value of 
indicates that and tend to vary together. A negative value indicates value indicates that when one variable is increasing the other tends to be decreasing. A zero value of indicates that there is no correlation between and . As will be seen below, it does not mean that they are statistically independent.
It is sometimes more convinient to deal with values of the cross-variances which have ben normalized by the appropriate variances. Thus the correlation coefficient is defined as:
The bi-variate normal (or Gaussian) distribution
dssd
